Trajectory Tracking and Obstacle Avoidance Control of Unmanned Underwater Vehicles Based on MPC

Sun, Bing; Zhang, Wei; Song, Aiguo; Zhu, Xinlei; Zhu, Daqi

doi:10.1109/usys.2018.8779160

Cited by 11 publications

(14 citation statements)

References 9 publications

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“…Using the predicted state and output trajectories defined by Eqs. (35) and (38), respectively, as well as the relations (41), (49)-(50) becomê…”

Section: The Mpc-sl Quadratic Optimization Problemmentioning

confidence: 85%

“…When other types of constraints imposed on predicted state and/or output variables are necessary, in the MPC-SL algorithm we have to find their linear approximations by means of general Eqs. (35) and (38), respectively.…”

Section: The Mpc-sl Quadratic Optimization Problemmentioning

confidence: 99%

“…A different method is to utilize a fuzzy controller [35]. Let us emphasize the fact that the problem of collision avoidance is important not only in vehicle applications, but also in the case of Unmanned aerial vehicles (UAVs) [36], wheeled mobile robots [37] or unmanned underwater vehicles [38].…”

Section: Introductionmentioning

confidence: 99%

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Computationally Simple Nonlinear MPC Algorithm for Vehicle Obstacle Avoidance With Minimization of Fuel Utilization

Nebeluk

Ławryńczuk

2021

IEEE Access

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This work is concerned with Model Predictive Control (MPC) algorithm for vehicle obstacle avoidance. The second objective of the algorithm is on-line minimization of fuel utilization. At first, the rudimentary nonlinear MPC optimization problem is formulated. Next, the constraints related to the predicted process state variables are formulated as soft ones to guarantee computational safety. Furthermore, in order to obtain a computationally simple procedure, the process dynamics and the fuel utilization model are linearized on-line and used for prediction in MPC. It leads to a quadratic programming MPC task, the necessity of nonlinear optimization performed in real-time is eliminated. In order to stress advantages of the discussed computationally uncomplicated MPC method it is compared with the basic scheme with on-line nonlinear optimization in terms of control quality and computational time. Additionally, effectiveness of the MPC algorithm is discussed in presence of modeling errors and measurement noise. Finally, additional constraints imposed on the rate of change of the manipulated variables are considered.

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“…Using the predicted state and output trajectories defined by Eqs. (35) and (38), respectively, as well as the relations (41), (49)-(50) becomê…”

Section: The Mpc-sl Quadratic Optimization Problemmentioning

confidence: 85%

Section: The Mpc-sl Quadratic Optimization Problemmentioning

confidence: 99%

See 1 more Smart Citation

Computationally Simple Nonlinear MPC Algorithm for Vehicle Obstacle Avoidance With Minimization of Fuel Utilization

Nebeluk

Ławryńczuk

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…By this receding optimization algorithm and the set constraints, online control can be realized and excessive velocity results are avoided. Sun et al applied MPC as the vehicle trajectory tracking control, achieving satisfactory tracking results with fewer and gentler fluctuations, which demonstrates the effectiveness of MPC [85] .…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…In the backstepping method, control functions for each subsystem are designed based on the Lyapunov techniques and generated to form the complete control law [84] . However, the actuator saturation is induced by the speed-jump problem, which usually occurs in the backstepping control methods for trajectory tracking [85] .…”

Section: Backstepping Controlmentioning

confidence: 99%

Motion planning and tracking control of unmanned underwater vehicles: technologies, challenges and prospects

Zhu¹,

Yan²,

Yang³

2022

Intell Robot

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The motion planning and tracking control techniques of unmanned underwater vehicles (UUV) are fundamentally significant for efficient and robust UUV navigation, which is crucial for underwater rescue, facility maintenance, marine resource exploration, aquatic recreation, etc. Studies on UUV motion planning and tracking control have been growing rapidly worldwide, which are usually sorted into the following topics: task assignment of the multi-UUV system, UUV path planning, and UUV trajectory tracking. This paper provides a comprehensive review of conventional and intelligent technologies for motion planning and tracking control of UUVs. Analysis of the benefits and drawbacks of these various methodologies in literature is presented. In addition, the challenges and prospects of UUV motion planning and tracking control are provided as possible developments for future research.

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Model predictive cascade control for resolving actuator saturation in human‐occupied vehicle trajectory tracking

Zhu,

Wang,

Zhang

et al. 2023

Intl J Robust & Nonlinear

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SummaryA model predictive cascade controller is proposed in this article to resolve the actuator saturation problem of underwater human‐occupied vehicle (HOV) trajectory tracking. For the kinematic model, a grasshopper optimization‐based model predictive control (MPC) is developed to obtain control velocities with small fluctuations in allowable regions. Based on the desired velocities obtained from the improved kinematic control, the HOV dynamic model is used to generate the corresponding torques and forces using sliding mode control. With the control velocities and forces given by the cascade control strategy, the elimination of actuator saturation during the HOV trajectory tracking can be realized. The proposed controller is verified by simulations using a numerical example based on a four degrees of freedom (4‐DOFs) HOV.

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Trajectory Tracking and Obstacle Avoidance Control of Unmanned Underwater Vehicles Based on MPC

Cited by 11 publications

References 9 publications

Computationally Simple Nonlinear MPC Algorithm for Vehicle Obstacle Avoidance With Minimization of Fuel Utilization

Computationally Simple Nonlinear MPC Algorithm for Vehicle Obstacle Avoidance With Minimization of Fuel Utilization

Motion planning and tracking control of unmanned underwater vehicles: technologies, challenges and prospects

Model predictive cascade control for resolving actuator saturation in human‐occupied vehicle trajectory tracking

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